Chaos Engineer

When to Use This Skill

• Designing and executing chaos experiments
• Implementing failure injection frameworks (Chaos Monkey, Litmus, etc.)
• Planning and conducting game day exercises
• Building blast radius controls and safety mechanisms
• Setting up continuous chaos testing in CI/CD
• Improving system resilience based on experiment findings

Core Workflow

1. System Analysis - Map architecture, dependencies, critical paths, and failure modes
2. Experiment Design - Define hypothesis, steady state, blast radius, and safety controls
3. Execute Chaos - Run controlled experiments with monitoring and quick rollback
4. Learn & Improve - Document findings, implement fixes, enhance monitoring
5. Automate - Integrate chaos testing into CI/CD for continuous resilience

Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When | |-------|-----------|-----------| | Experiments | references/experiment-design.md | Designing hypothesis, blast radius, rollback | | Infrastructure | references/infrastructure-chaos.md | Server, network, zone, region failures | | Kubernetes | references/kubernetes-chaos.md | Pod, node, Litmus, chaos mesh experiments | | Tools & Automation | references/chaos-tools.md | Chaos Monkey, Gremlin, Pumba, CI/CD integration | | Game Days | references/game-days.md | Planning, executing, learning from game days |

Safety Checklist

Non-obvious constraints that must be enforced on every experiment:

• Steady state first — define and verify baseline metrics before injecting any failure
• Blast radius cap — start with the smallest possible impact scope; expand only after validation
• Automated rollback ≤ 30 seconds — abort path must be scripted and tested before the experiment begins
• Single variable — change only one failure condition at a time until behaviour is well understood
• No production without safety nets — customer-facing environments require circuit breakers, feature flags, or canary isolation
• Close the loop — every experiment must produce a written learning summary and at least one tracked improvement

Output Templates

When implementing chaos engineering, provide:

1. Experiment design document (hypothesis, metrics, blast radius)
2. Implementation code (failure injection scripts/manifests)
3. Monitoring setup and alert configuration
4. Rollback procedures and safety controls
5. Learning summary and improvement recommendations

Concrete Example: Pod Failure Experiment (Litmus Chaos)

The following shows a complete experiment — from hypothesis to rollback — using Litmus Chaos on Kubernetes.

Step 1 — Define steady state and apply the experiment

bash
# Verify baseline: p99 latency < 200ms, error rate < 0.1%
kubectl get deploy my-service -n production
kubectl top pods -n production -l app=my-service

Step 2 — Create and apply a Litmus ChaosEngine manifest

yaml
# chaos-pod-delete.yaml
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
  name: my-service-pod-delete
  namespace: production
spec:
  appinfo:
    appns: production
    applabel: "app=my-service"
    appkind: deployment
  # Limit blast radius: only 1 replica at a time
  engineState: active
  chaosServiceAccount: litmus-admin
  experiments:
    - name: pod-delete
      spec:
        components:
          env:
            - name: TOTAL_CHAOS_DURATION
              value: "60"          # seconds
            - name: CHAOS_INTERVAL
              value: "20"          # delete one pod every 20s
            - name: FORCE
              value: "false"
            - name: PODS_AFFECTED_PERC
              value: "33"          # max 33% of replicas affected

bash
# Apply the experiment
kubectl apply -f chaos-pod-delete.yaml

# Watch experiment status
kubectl describe chaosengine my-service-pod-delete -n production
kubectl get chaosresult my-service-pod-delete-pod-delete -n production -w

Step 3 — Monitor during the experiment

bash
# Tail application logs for errors
kubectl logs -l app=my-service -n production --since=2m -f

# Check ChaosResult verdict when complete
kubectl get chaosresult my-service-pod-delete-pod-delete \
  -n production -o jsonpath='{.status.experimentStatus.verdict}'

Step 4 — Rollback / abort if steady state is violated

bash
# Immediately stop the experiment
kubectl patch chaosengine my-service-pod-delete \
  -n production --type merge -p '{"spec":{"engineState":"stop"}}'

# Confirm all pods are healthy
kubectl rollout status deployment/my-service -n production

Concrete Example: Network Latency with toxiproxy

bash
# Install toxiproxy CLI
brew install toxiproxy   # macOS; use the binary release on Linux

# Start toxiproxy server (runs alongside your service)
toxiproxy-server &

# Create a proxy for your downstream dependency
toxiproxy-cli create -l 0.0.0.0:22222 -u downstream-db:5432 db-proxy

# Inject 300ms latency with 10% jitter — blast radius: this proxy only
toxiproxy-cli toxic add db-proxy -t latency -a latency=300 -a jitter=30

# Run your load test / observe metrics here ...

# Remove the toxic to restore normal behaviour
toxiproxy-cli toxic remove db-proxy -n latency_downstream

Concrete Example: Chaos Monkey (Spinnaker / standalone)

bash
# chaos-monkey-config.yml — restrict to a single ASG
deployment:
  enabled: true
  regionIndependence: false
chaos:
  enabled: true
  meanTimeBetweenKillsInWorkDays: 2
  minTimeBetweenKillsInWorkDays: 1
  grouping: APP           # kill one instance per app, not per cluster
  exceptions:
    - account: production
      region: us-east-1
      detail: "*-canary"  # never kill canary instances

# Apply and trigger a manual kill for testing
chaos-monkey --app my-service --account staging --dry-run false

Documentation